1 Overview

1.1 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 846) ranged in age 18 to 79 (Mage = 37.21, SD = 13.58). The participants were predominantly female (56.58%). The participants received £0.35 ($0.45) for successfully completing the task.

1.2 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Randomisation check
Item Dynamic Static No norm Significance test
Age (years) 37.34 $\pm$ 14.22 37.90 $\pm$ 12.97 36.40 $\pm$ 13.55 $F(2, 843) = 0.89$, $\mathit{MSE} = 184.49$, $p = .409$
Gender (\%) 1 (39.49\%) 2 (60.14\%) 3 (0.36\%) 1 (41.9\%) 2 (58.1\%) 1 (48.25\%) 2 (51.05\%) 3 (0.7\%) $\chi^2(4, n = 846) = 6.92$, $p = .140$
Political position 3.47 $\pm$ 1.22 3.54 $\pm$ 1.26 3.45 $\pm$ 1.26 $F(2, 843) = 0.39$, $\mathit{MSE} = 1.55$, $p = .679$
Nationality (\%) 1 (84.06\%) 2 (3.62\%) 3 (9.78\%) 4 (0.72\%) NA (1.81\%) 1 (80.99\%) 2 (5.28\%) 3 (8.45\%) 4 (1.41\%) NA (3.87\%) 1 (80.77\%) 2 (5.94\%) 3 (8.74\%) 4 (1.75\%) NA (2.8\%) $\chi^2(6, n = 846) = 3.23$, $p = .779$

1.3 Correlations

Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures
Alpha M SD 1 2 3 4 5 6
Interest - 3.58 1.82
Attitude 0.90 4.64 1.29 .80**
Intention 0.98 4.22 1.80 .83** .82**
Expectation 0.99 3.93 1.75 .80** .80** .92**
Intent/expectation composite - 4.08 1.74 .83** .83** .98** .98**
Perception of change - 5.14 0.90 .27** .26** .27** .25** .27**
Preconformity - 4.18 1.20 .44** .40** .39** .37** .39** .37**

2 Confirmatory analyses

2.1 1. Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing their meat consumption (compared to static norm)?

2.1.1 Effect of condition on interest in reducing meat consumption

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from Mdiff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

The mean interest for participants in the dynamic norm condition was M = 3.64 (SD = 1.83), and the mean interest in the static norm condition was M = 3.68 (SD = 1.84). The mean interest in the no norm condition was M = 3.41 (SD = 1.77).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.64, SD = 1.83) and static norm (M = 3.68, SD = 1.84) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.27]\), \(t(843) = -0.23\), \(p = .821\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.18, RR[0.65, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.41, SD = 1.77) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.23\), 95% CI \([-0.07\), \(0.53]\), \(t(843) = 1.52\), \(p = .130\), d = 0.13, or the static-norm condition \(\Delta M = 0.27\), 95% CI \([-0.03\), \(0.57]\), \(t(843) = 1.76\), \(p = .080\), d = 0.15. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.10\), 95% CI \([-0.16\), \(0.36]\), \(t(843) = 0.74\), \(p = .458\).

2.1.2 Effect of demographic variables and condition on interest

Political left-wing participants were more interested than were right wing participants, \(t(844) = -7.23\), \(p < .001\), and women were more interested than were men, \(\Delta M = -0.50\), 95% CI \([-0.75\), \(-0.26]\), \(t(781.22) = -4.00\), \(p < .001\). When we controlled for these factors, the effect of the dynamic-norm condition (compared with that of the static-norm condition) on interest in eating less meat was \(b = -0.27\), 95% CI \([-1.25\), \(0.72]\), \(t(553) = -0.53\), \(p = .594\).

2.2 2. Will participants in the dynamic norm condition be more likely (than static norm and control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of Mdiff = 0.40. The plausible maximum effect was set at twice the predicted effect of Mdiff = 0.80. A Bayes factor was calculated for each test.

Expectations of future meat consumption
Future Norm
Preconformity
Combined
Condition $n$ $M$ $SD$ $M$ $SD$ $M$ $SD$
Dynamic 276 5.26 0.93 4.35 1.18 4.81 0.88
Static 284 5.20 0.85 4.24 1.19 4.72 0.85
No norm 286 4.97 0.89 3.95 1.18 4.46 0.84

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.06\), 95% CI \([-0.08\), \(0.21]\), \(t(843) = 0.85\), \(p = .397\), d = 0.07, \(B_{\text{HN}(0, 0.40)}\) = 0.42, RR[0.05, 0.8]

Perception change contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.06 $[-0.08$, $0.21]$ 0.85 .397
DYNO Dynamic, control 0.30 $[0.15$, $0.44]$ 3.94 < .001
STNO Static, control 0.23 $[0.09$, $0.38]$ 3.11 .002
DYCONT Dynamic, both 0.18 $[0.05$, $0.31]$ 2.75 .006
EXPNO Norms, control -0.26 $[-0.39$, $-0.14]$ -4.08 < .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.35, SD = 1.18) and static norm (M = 4.24, SD = 1.19) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(843) = 1.08\), \(p = .279\), d = 0.09, \(B_{\text{HN}(0, 0.40)}\) = 0.72, RR[0.05, 1.5].

Preconformity contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.11 $[-0.09$, $0.31]$ 1.08 .279
DYNO Dynamic, control 0.40 $[0.20$, $0.60]$ 4.00 < .001
STNO Static, control 0.29 $[0.10$, $0.49]$ 2.94 .003
DYCONT Dynamic, both 0.25 $[0.08$, $0.43]$ 2.93 .004
EXPNO Norms, control -0.35 $[-0.52$, $-0.18]$ -4.02 < .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.88) and static norm (M = 4.72, SD = 0.85) conditions, \(\Delta M = 0.09\), 95% CI \([-0.06\), \(0.23]\), \(t(843) = 1.19\), \(p = .235\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.62, RR[0.05, 1.25].

Combined contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.09 $[-0.06$, $0.23]$ 1.19 .235
DYNO Dynamic, control 0.35 $[0.21$, $0.49]$ 4.81 < .001
STNO Static, control 0.26 $[0.12$, $0.40]$ 3.65 < .001
DYCONT Dynamic, both 0.22 $[0.09$, $0.34]$ 3.45 .001
EXPNO Norms, control -0.31 $[-0.43$, $-0.18]$ -4.89 < .001

3 Secondary analyses

3.1 1. Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 129.959 p-value lower bound: 0e+00 t-value upper bound: \(-23.608\) p-value upper bound: \(2.99\times10^{-86}\) degrees of freedom : 557.66

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 3.355 upper bound 90% CI: 3.57

NHST confidence interval: lower bound 95% CI: 3.335 upper bound 95% CI: 3.591

Equivalence Test Result: The equivalence test was significant, t(557.66) = \(-23.608\), p = \(2.99\times10^{-86}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(557.66) = 53.175, p = \(1.51\times10^{-220}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.1.1 2. Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 15.086 p-value lower bound: \(1.12\times10^{-43}\) t-value upper bound: \(-13.542\) p-value upper bound: \(1.25\times10^{-36}\) degrees of freedom : 557.55

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.306\) upper bound 90% CI: 0.845

NHST confidence interval: lower bound 95% CI: \(-0.417\) upper bound 95% CI: 0.956

Equivalence Test Result: The equivalence test was significant, t(557.55) = \(-13.542\), p = \(1.25\times10^{-36}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(557.55) = 0.772, p = .441, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory Analyses

4.1 3. Does dynamic norm (versus static or no norm) information increase participants’ positive attitude, intentions, and expectations to reduce their meat consumption?

4.1.1 Convergence

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4.1.2 Estimates

Posterior results for simple model (H3)
Model 1: Uninformative priors$^a$
Model 2: Weakly informative priors$^b$
Model 3: Moderately informative priors$^c$
Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest 0.040 (0.156) -0.265, 0.349 819.155 1.003 normal(0,10) 0.089 (0.154) -0.212, 0.388 771.276 1.005 122.5 normal(0.5,0.75) 0.196 (0.132) -0.059, 0.459 889.294 1.004 390 normal(0.5, 0.35)
Attitude -0.049 (0.111) -0.267, 0.165 771.073 1.004 normal(0,10) -0.012 (0.112) -0.234, 0.207 730.007 1.005 -75.51 normal(0.5,0.75) 0.064 (0.093) -0.117, 0.244 928.977 1.003 -230.61 normal(0.5, 0.35)
Intention/Expectation -0.036 (0.144) -0.314, 0.251 808.662 1.005 normal(0,10) 0.011 (0.145) -0.271, 0.291 725.346 1.006 -130.56 normal(0.5,0.75) 0.113 (0.122) -0.12, 0.353 931.226 1.004 -413.89 normal(0.5, 0.35)
Note.
PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = 0.498666666666667
b ppp = 0.493
c ppp = 0.437333333333333

4.2 4. How do demographic factors such as age, gender, and political position predict primary dependent variables relating to meat consumption?

4.2.1 Convergence

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4.2.2 Estimates

Posterior results for full model (H4)
Model 1: Uninformative priors (ppp = 0.486)
Model 2: Informative priors (ppp = 0.471)
Model 3: Informative priors (ppp = 0.421)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest
Condition 0.063 (0.147) -0.211, 0.354 1166.766 1 normal(0,10) 0.100 (0.142) -0.175, 0.372 1433.183 1.003 58.73 normal(0.5,0.75) 0.208 (0.130) -0.048, 0.475 1593.563 1 230.16 normal(0.5,0.35)
Age -0.002 (0.006) -0.013, 0.009 1873.321 1.001 normal(0,10) -0.002 (0.006) -0.013, 0.009 1905.213 1.001 0 normal(0,10) -0.002 (0.006) -0.014, 0.009 1954.115 0.999 0 normal(0,10)
Gender 0.524 (0.158) 0.204, 0.826 1197.542 1.001 normal(0,10) 0.525 (0.154) 0.224, 0.83 1055.009 1.002 0.19 normal(0,10) 0.524 (0.156) 0.213, 0.821 1167.231 1.004 0 normal(0,10)
Politics -0.308 (0.063) -0.43, -0.186 1210.214 1 normal(0,10) -0.311 (0.062) -0.43, -0.189 1240.268 1.004 0.97 normal(0,10) -0.312 (0.059) -0.425, -0.197 1361.417 1.003 1.3 normal(0,10)
Attitudes
Condition -0.033 (0.105) -0.233, 0.176 1163.076 1 normal(0,10) -0.008 (0.103) -0.21, 0.191 1381.633 1.003 -75.76 normal(0.5,0.75) 0.071 (0.094) -0.106, 0.26 1595.695 1.001 -315.15 normal(0.5,0.35)
Age 0.001 (0.004) -0.007, 0.009 2020.368 1 normal(0,10) 0.001 (0.004) -0.007, 0.009 2014.231 1.001 0 normal(0,10) 0.001 (0.004) -0.008, 0.009 1924.127 1 0 normal(0,10)
Gender 0.299 (0.112) 0.081, 0.518 1252.849 1.002 normal(0,10) 0.299 (0.110) 0.088, 0.511 1086.499 1.002 0 normal(0,10) 0.299 (0.112) 0.078, 0.513 1193.494 1.004 0 normal(0,10)
Politics -0.242 (0.045) -0.328, -0.152 1135.188 1.001 normal(0,10) -0.243 (0.045) -0.331, -0.156 1255.325 1.004 0.41 normal(0,10) -0.245 (0.043) -0.327, -0.16 1354.755 1.001 1.24 normal(0,10)
Intentions
Condition -0.018 (0.139) -0.278, 0.252 1159.325 1 normal(0,10) 0.014 (0.136) -0.255, 0.273 1470.680 1.003 -177.78 normal(0.5,0.75) 0.123 (0.124) -0.117, 0.369 1452.978 1.002 -783.33 normal(0.5,0.35)
Age 0.008 (0.005) -0.002, 0.018 1905.898 1.001 normal(0,10) 0.008 (0.005) -0.002, 0.019 1952.531 1.001 0 normal(0,10) 0.008 (0.005) -0.003, 0.018 1884.567 1 0 normal(0,10)
Gender 0.592 (0.148) 0.306, 0.88 1275.347 1.001 normal(0,10) 0.594 (0.145) 0.318, 0.881 1084.447 1.001 0.34 normal(0,10) 0.595 (0.147) 0.303, 0.874 1200.347 1.004 0.51 normal(0,10)
Politics -0.272 (0.060) -0.389, -0.156 1123.352 1.001 normal(0,10) -0.274 (0.059) -0.391, -0.158 1240.672 1.003 0.74 normal(0,10) -0.275 (0.057) -0.383, -0.161 1303.289 1.002 1.1 normal(0,10)
Expectations
Condition 0.063 (0.147) -0.211, 0.354 1166.766 1 normal(0,10) 0.100 (0.142) -0.175, 0.372 1433.183 1.003 58.73 normal(0.5,0.75) 3.148 (0.130) -0.048, 0.475 1593.563 1 230.16 normal(0.5,0.35)
Age -0.002 (0.006) -0.013, 0.009 1873.321 1.001 normal(0,10) -0.002 (0.006) -0.013, 0.009 1905.213 1.001 0 normal(0,10) 1.614 (0.006) -0.014, 0.009 1954.115 0.999 0 normal(0,10)
Gender 0.524 (0.158) 0.204, 0.826 1197.542 1.001 normal(0,10) 0.525 (0.154) 0.224, 0.83 1055.009 1.002 0.19 normal(0,10) 2.823 (0.156) 0.213, 0.821 1167.231 1.004 0 normal(0,10)
Politics -0.308 (0.063) -0.43, -0.186 1210.214 1 normal(0,10) -0.311 (0.062) -0.43, -0.189 1240.268 1.004 0.97 normal(0,10) 1.786 (0.059) -0.425, -0.197 1361.417 1.003 1.3 normal(0,10)

4.3 5. How does age interact with norm condition to influence dependent variables?

4.3.1 Convergence

[[1]]

[[2]]

[[3]]

4.3.2 Estimates

Posterior results for multi-sample analysis by age (H4)
Model 1: Uninformative priors
Model 2: Informative priors
Model 3: Informative priors
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Older
Interest 0.127 (0.223) -0.307, 0.553 917.266 1.002 normal(0,10) 0.200 (0.206) -0.206, 0.601 930.554 1.001 57.48 normal(0.5,0.75) 0.362 (0.172) 0.041, 0.705 939.531 1.006 185.04 normal(0.5,0.35)
Attitude 0.035 (0.162) -0.276, 0.361 978.182 1.004 normal(0,10) 0.086 (0.149) -0.198, 0.375 961.888 1 145.71 normal(0.5,0.75) 0.208 (0.129) -0.037, 0.463 933.096 1.01 494.29 normal(0.5,0.35)
Intention -0.072 (0.214) -0.508, 0.35 849.457 1.004 normal(0,10) 0.004 (0.194) -0.376, 0.381 896.109 1 -105.56 normal(0.5,0.75) 0.170 (0.163) -0.156, 0.489 902.042 1.008 -336.11 normal(0.5,0.35)
Expectation 0.027 (0.224) -0.417, 0.456 838.891 1.002 normal(0,10) 0.120 (0.202) -0.275, 0.508 867.831 1.002 344.44 normal(0.5,0.75) 0.288 (0.164) -0.025, 0.606 963.833 1.003 966.67 normal(0.5,0.35)
Younger
Interest -0.086 (0.160) -0.4, 0.222 863.224 1.001 normal(0,10) -0.019 (0.148) -0.308, 0.271 910.991 1.002 -77.91 normal(0.5,0.75) 0.103 (0.120) -0.135, 0.334 891.487 1.002 -219.77 normal(0.5,0.35)
Attitude -0.012 (0.214) -0.435, 0.402 846.377 1.001 normal(0,10) 0.073 (0.193) -0.297, 0.457 891.293 1.002 -708.33 normal(0.5,0.75) 0.233 (0.157) -0.075, 0.532 866.660 1.004 -2041.67 normal(0.5,0.35)
Intention 0.127 (0.223) -0.307, 0.553 917.266 1.002 normal(0,10) 0.200 (0.206) -0.206, 0.601 930.554 1.001 57.48 normal(0.5,0.75) 0.362 (0.172) 0.041, 0.705 939.531 1.006 185.04 normal(0.5,0.35)
Expectation 0.035 (0.162) -0.276, 0.361 978.182 1.004 normal(0,10) 0.086 (0.149) -0.198, 0.375 961.888 1 145.71 normal(0.5,0.75) 0.208 (0.129) -0.037, 0.463 933.096 1.01 494.29 normal(0.5,0.35)

5 Environment and data

5.0.1 Session information

─ Session info ──────────────────────────────────────────────────────────────────────────────────────────

─ Packages ──────────────────────────────────────────────────────────────────────────────────────────────
 package      * version    date       lib source                           
 abind          1.4-5      2016-07-21 [1] CRAN (R 4.0.2)                   
 assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.2)                   
 backports      1.2.1      2020-12-09 [1] CRAN (R 4.0.2)                   
 bayesplot    * 1.8.0      2021-01-10 [1] CRAN (R 4.0.2)                   
 bfrr         * 0.0.0.9000 2020-10-07 [1] Github (debruine/bfrr@9b80a99)   
 bitops         1.0-6      2013-08-17 [1] CRAN (R 4.0.2)                   
 blavaan      * 0.3-16.745 2021-04-22 [1] Github (ecmerkle/blavaan@c69efe7)
 boot           1.3-27     2021-02-12 [1] CRAN (R 4.0.2)                   
 broom          0.7.6      2021-04-05 [1] CRAN (R 4.0.2)                   
 bslib          0.2.4      2021-01-25 [1] CRAN (R 4.0.2)                   
 cachem         1.0.4      2021-02-13 [1] CRAN (R 4.0.2)                   
 callr          3.7.0      2021-04-20 [1] CRAN (R 4.0.4)                   
 car            3.0-10     2020-09-29 [1] CRAN (R 4.0.2)                   
 carData        3.0-4      2020-05-22 [1] CRAN (R 4.0.2)                   
 cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.0.2)                   
 checkmate      2.0.0      2020-02-06 [1] CRAN (R 4.0.2)                   
 cli            2.4.0      2021-04-05 [1] CRAN (R 4.0.2)                   
 coda           0.19-4     2020-09-30 [1] CRAN (R 4.0.2)                   
 codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.0.2)                   
 codetools      0.2-18     2020-11-04 [1] CRAN (R 4.0.4)                   
 colorspace     2.0-0      2020-11-11 [1] CRAN (R 4.0.3)                   
 CompQuadForm   1.4.3      2017-04-12 [1] CRAN (R 4.0.2)                   
 conquer        1.0.2      2020-08-27 [1] CRAN (R 4.0.2)                   
 crayon         1.4.1      2021-02-08 [1] CRAN (R 4.0.2)                   
 curl           4.3        2019-12-02 [1] CRAN (R 4.0.1)                   
 data.table     1.14.0     2021-02-21 [1] CRAN (R 4.0.2)                   
 DBI            1.1.1      2021-01-15 [1] CRAN (R 4.0.2)                   
 dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.0.2)                   
 desc           1.3.0      2021-03-05 [1] CRAN (R 4.0.2)                   
 devtools       2.4.0      2021-04-07 [1] CRAN (R 4.0.2)                   
 digest         0.6.27     2020-10-24 [1] CRAN (R 4.0.2)                   
 dplyr        * 1.0.5      2021-03-05 [1] CRAN (R 4.0.2)                   
 ellipsis       0.3.1      2020-05-15 [1] CRAN (R 4.0.2)                   
 emmeans      * 1.5.5-1    2021-03-21 [1] CRAN (R 4.0.2)                   
 estimability   1.3        2018-02-11 [1] CRAN (R 4.0.2)                   
 evaluate       0.14       2019-05-28 [1] CRAN (R 4.0.1)                   
 ez             4.4-0      2016-11-02 [1] CRAN (R 4.0.2)                   
 fansi          0.4.2      2021-01-15 [1] CRAN (R 4.0.2)                   
 farver         2.1.0      2021-02-28 [1] CRAN (R 4.0.2)                   
 fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.0.2)                   
 forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.0.2)                   
 foreign        0.8-81     2020-12-22 [1] CRAN (R 4.0.4)                   
 fs             1.5.0      2020-07-31 [1] CRAN (R 4.0.2)                   
 future         1.21.0     2020-12-10 [1] CRAN (R 4.0.3)                   
 future.apply   1.7.0      2021-01-04 [1] CRAN (R 4.0.2)                   
 generics       0.1.0      2020-10-31 [1] CRAN (R 4.0.2)                   
 ggplot2      * 3.3.3      2020-12-30 [1] CRAN (R 4.0.2)                   
 ggpubr       * 0.4.0      2020-06-27 [1] CRAN (R 4.0.2)                   
 ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.0.2)                   
 ggsignif       0.6.1      2021-02-23 [1] CRAN (R 4.0.2)                   
 globals        0.14.0     2020-11-22 [1] CRAN (R 4.0.2)                   
 glue           1.4.2      2020-08-27 [1] CRAN (R 4.0.2)                   
 gridExtra      2.3        2017-09-09 [1] CRAN (R 4.0.2)                   
 gtable         0.3.0      2019-03-25 [1] CRAN (R 4.0.2)                   
 haven          2.4.0      2021-04-14 [1] CRAN (R 4.0.2)                   
 here         * 1.0.1      2020-12-13 [1] CRAN (R 4.0.2)                   
 highr          0.9        2021-04-16 [1] CRAN (R 4.0.2)                   
 hms            1.0.0      2021-01-13 [1] CRAN (R 4.0.2)                   
 htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.0.2)                   
 httr           1.4.2      2020-07-20 [1] CRAN (R 4.0.2)                   
 inline         0.3.17     2020-12-01 [1] CRAN (R 4.0.2)                   
 insight        0.13.2     2021-04-01 [1] CRAN (R 4.0.2)                   
 jquerylib      0.1.3      2020-12-17 [1] CRAN (R 4.0.2)                   
 jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.0.2)                   
 kableExtra   * 1.3.4      2021-02-20 [1] CRAN (R 4.0.2)                   
 knitr        * 1.32       2021-04-14 [1] CRAN (R 4.0.2)                   
 labeling       0.4.2      2020-10-20 [1] CRAN (R 4.0.2)                   
 labelled       2.8.0      2021-03-08 [1] CRAN (R 4.0.2)                   
 lattice        0.20-41    2020-04-02 [1] CRAN (R 4.0.4)                   
 lavaan       * 0.6-8      2021-03-10 [1] CRAN (R 4.0.2)                   
 lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.0.2)                   
 listenv        0.8.0      2019-12-05 [1] CRAN (R 4.0.2)                   
 lme4           1.1-26     2020-12-01 [1] CRAN (R 4.0.2)                   
 loo            2.4.1      2020-12-09 [1] CRAN (R 4.0.2)                   
 lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.0.2)                   
 magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.0.2)                   
 MASS         * 7.3-53.1   2021-02-12 [1] CRAN (R 4.0.2)                   
 Matrix         1.3-2      2021-01-06 [1] CRAN (R 4.0.4)                   
 MatrixModels   0.5-0      2021-03-02 [1] CRAN (R 4.0.2)                   
 matrixStats    0.58.0     2021-01-29 [1] CRAN (R 4.0.2)                   
 MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.0.2)                   
 mcmc           0.9-7      2020-03-21 [1] CRAN (R 4.0.2)                   
 MCMCpack       1.5-0      2021-01-20 [1] CRAN (R 4.0.2)                   
 memoise        2.0.0      2021-01-26 [1] CRAN (R 4.0.2)                   
 mgcv           1.8-35     2021-04-18 [1] CRAN (R 4.0.2)                   
 minqa          1.2.4      2014-10-09 [1] CRAN (R 4.0.2)                   
 mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.0.2)                   
 modelr         0.1.8      2020-05-19 [1] CRAN (R 4.0.2)                   
 MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.0.2)                   
 munsell        0.5.0      2018-06-12 [1] CRAN (R 4.0.2)                   
 mvtnorm        1.1-1      2020-06-09 [1] CRAN (R 4.0.2)                   
 nlme           3.1-152    2021-02-04 [1] CRAN (R 4.0.4)                   
 nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.0.2)                   
 NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.0.2)                   
 nonnest2       0.5-5      2020-07-05 [1] CRAN (R 4.0.2)                   
 openxlsx       4.2.3      2020-10-27 [1] CRAN (R 4.0.2)                   
 papaja       * 0.1.0.9997 2020-08-11 [1] Github (crsh/papaja@0457653)     
 parallelly     1.24.0     2021-03-14 [1] CRAN (R 4.0.2)                   
 pbivnorm       0.6.0      2015-01-23 [1] CRAN (R 4.0.2)                   
 pillar         1.6.0      2021-04-13 [1] CRAN (R 4.0.2)                   
 pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.0.2)                   
 pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.0.2)                   
 pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.0.2)                   
 plyr           1.8.6      2020-03-03 [1] CRAN (R 4.0.2)                   
 prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.0.2)                   
 processx       3.5.1      2021-04-04 [1] CRAN (R 4.0.2)                   
 ps             1.6.0      2021-02-28 [1] CRAN (R 4.0.2)                   
 psy          * 1.1        2012-06-21 [1] CRAN (R 4.0.2)                   
 psych        * 2.1.3      2021-03-27 [1] CRAN (R 4.0.2)                   
 purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.0.2)                   
 qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.0.2)                   
 quantreg       5.85       2021-02-24 [1] CRAN (R 4.0.2)                   
 R6             2.5.0      2020-10-28 [1] CRAN (R 4.0.2)                   
 RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.0.2)                   
 Rcpp         * 1.0.6      2021-01-15 [1] CRAN (R 4.0.2)                   
 RcppParallel * 5.1.2      2021-04-15 [1] CRAN (R 4.0.2)                   
 RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.0.2)                   
 readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.0.2)                   
 readxl         1.3.1      2019-03-13 [1] CRAN (R 4.0.2)                   
 remotes        2.3.0      2021-04-01 [1] CRAN (R 4.0.2)                   
 reprex         2.0.0      2021-04-02 [1] CRAN (R 4.0.2)                   
 reshape        0.8.8      2018-10-23 [1] CRAN (R 4.0.2)                   
 reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.0.2)                   
 rio            0.5.26     2021-03-01 [1] CRAN (R 4.0.2)                   
 rlang        * 0.4.10     2020-12-30 [1] CRAN (R 4.0.2)                   
 rmarkdown      2.7        2021-02-19 [1] CRAN (R 4.0.2)                   
 rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.0.3)                   
 rstan          2.21.2     2020-07-27 [1] CRAN (R 4.0.3)                   
 rstantools     2.1.1      2020-07-06 [1] CRAN (R 4.0.2)                   
 rstatix        0.7.0      2021-02-13 [1] CRAN (R 4.0.2)                   
 rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.0.2)                   
 rvest          1.0.0      2021-03-09 [1] CRAN (R 4.0.2)                   
 sandwich       3.0-0      2020-10-02 [1] CRAN (R 4.0.2)                   
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 usethis        2.0.1      2021-02-10 [1] CRAN (R 4.0.2)                   
 utf8           1.2.1      2021-03-12 [1] CRAN (R 4.0.2)                   
 V8             3.4.0      2020-11-04 [1] CRAN (R 4.0.2)                   
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 yaml           2.2.1      2020-02-01 [1] CRAN (R 4.0.2)                   
 zip            2.1.1      2020-08-27 [1] CRAN (R 4.0.2)                   
 zoo            1.8-9      2021-03-09 [1] CRAN (R 4.0.2)                   

[1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

5.0.2 Codebook

name data_type ordered value_labels n_missing complete_rate n_unique empty top_counts min median max mean sd whitespace hist DYUK STUK NATIONALITY label
DYUK character NA NA 570 0.3262411 276 0 NA 7 NA 898 NA NA 0 NA Dynamic UK NA NA NA
STUK character NA NA 562 0.3356974 282 0 NA 3 NA 993 NA NA 0 NA NA Static UK NA NA
UKNATION factor FALSE
  1. 1,
  2. 2,
  3. 3,
  4. 4
24 0.9716312 4 NA 1: 693, 3: 76, 2: 42, 4: 11 NA NA NA NA NA NA NA NA NA NA NA
RESIDENT factor FALSE
  1. 1,
  • 2
  • 0 1.0000000 2 NA 1: 839, 2: 7 NA NA NA NA NA NA NA NA NA NA NA
    GENDER factor FALSE
    1. 1,
  • 2,
  • 3
  • 0 1.0000000 3 NA 2: 477, 1: 366, 3: 3 NA NA NA NA NA NA NA NA NA NA NA
    condition factor FALSE
    1. Dynamic,
  • Static,
  • No norm
  • 0 1.0000000 3 NA No : 286, Sta: 284, Dyn: 276 NA NA NA NA NA NA NA NA NA NA NA
    conditionbi factor FALSE
    1. Dynamic,
  • Static
  • 286 0.6619385 2 NA Sta: 284, Dyn: 276 NA NA NA NA NA NA NA NA NA NA NA
    genderbi factor FALSE
    1. 1,
  • 2
  • 3 0.9964539 2 NA 2: 477, 1: 366 NA NA NA NA NA NA NA NA NA NA NA
    agebi factor FALSE
    1. younger,
  • older
  • 0 1.0000000 2 NA you: 423, old: 423 NA NA NA NA NA NA NA NA NA NA NA
    NATIONALITY numeric NA NA 0 1.0000000 NA NA NA 3 27.0 183 28.1264775 11.9352031 NA ▇▁▁▁▁ NA NA Nationality NA
    INTEREST numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.5780142 1.8171931 NA ▇▅▅▃▅ NA NA NA NA
    ATT1 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.4739953 1.4647446 NA ▂▂▅▇▅ NA NA NA NA
    ATT2 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.5851064 1.3911693 NA ▂▂▆▇▆ NA NA NA NA
    ATT3 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.8758865 1.3734456 NA ▂▂▅▇▇ NA NA NA NA
    INTENT1 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1773050 1.8292186 NA ▆▃▃▇▇ NA NA NA NA
    INTENT2 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1501182 1.8072618 NA ▆▃▅▇▆ NA NA NA NA
    INTENT3 numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.3439716 1.8470845 NA ▆▂▃▇▇ NA NA NA NA
    EXPECT1 numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8959811 1.7864865 NA ▇▃▅▇▅ NA NA NA NA
    EXPECT2 numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8640662 1.7840077 NA ▇▃▅▇▅ NA NA NA NA
    EXPECT3 numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.0378251 1.7564049 NA ▆▅▅▇▆ NA NA NA NA
    PERCEPTNUM numeric NA NA 0 1.0000000 NA NA NA 3 30.0 85 28.7482270 11.1468611 NA ▂▇▂▁▁ NA NA NA NA
    PERCEPTSCALE numeric NA NA 0 1.0000000 NA NA NA 1 2.0 5 2.5685579 0.7485873 NA ▁▇▅▂▁ NA NA NA NA
    CONSTRUAL_1 numeric NA NA 0 1.0000000 NA NA NA 1 10.0 21 11.0955083 4.1522252 NA ▂▆▇▇▂ NA NA NA NA
    PERCEPTCHANGE numeric NA NA 0 1.0000000 NA NA NA 1 3.0 7 2.8605201 0.8980857 NA ▅▇▂▁▁ NA NA NA NA
    PRECONFORMITY numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.1796690 1.1959945 NA ▂▆▇▇▃ NA NA NA NA
    POLITICS numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.4905437 1.2448352 NA ▆▆▇▃▁ NA NA NA NA
    AGE numeric NA NA 0 1.0000000 NA NA NA 18 34.5 79 37.2080378 13.5810251 NA ▇▆▅▂▁ NA NA NA NA
    attitude_mean numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.6449961 1.2912197 NA ▁▂▅▇▃ NA NA NA NA
    intention_mean numeric NA NA 0 1.0000000 NA NA NA 1 4.7 7 4.2237983 1.7965065 NA ▅▃▃▇▆ NA NA NA NA
    expect_mean numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.9326241 1.7506056 NA ▆▅▅▇▅ NA NA NA NA
    expintent_avg numeric NA NA 0 1.0000000 NA NA NA 1 4.5 7 4.0782112 1.7381013 NA ▆▃▅▇▅ NA NA NA NA
    age_cent numeric NA NA 0 1.0000000 NA NA NA -19 -2.5 42 0.1955638 13.5810251 NA ▇▆▅▂▁ NA NA NA NA
    PERCEPTCHANGE_r numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 5.1394799 0.8980857 NA ▁▁▂▇▅ NA NA NA NA
    comb_future numeric NA NA 0 1.0000000 NA NA NA 2 4.5 7 4.6595745 0.8692358 NA ▁▆▇▅▁ NA NA NA NA
    No norm numeric NA NA 0 1.0000000 NA NA NA 0 0.0 1 0.3380615 0.4733294 NA ▇▁▁▁▅ NA NA NA NA
    Static numeric NA NA 0 1.0000000 NA NA NA 0 0.0 1 0.3356974 0.4725130 NA ▇▁▁▁▅ NA NA NA NA
    Dynamic numeric NA NA 0 1.0000000 NA NA NA 0 0.0 1 0.3262411 0.4691140 NA ▇▁▁▁▃ NA NA NA NA
    ---
title: "Results Notebook"
floatsintext : yes
output:
  html_notebook:
    toc: yes
    toc_float: yes
    toc_depth: 5
    code_folding: hide
    number_sections: yes
    fig_caption: yes
  bookdown::html_document2:
    toc: true
    toc_depth: 2
    toc_float: true
    highlight: pygments
    code_folding: hide
---

```{r setup, include = FALSE}
rmd_packages <- c("ggplot2", "papaja", "tidyverse", "kableExtra", "codebook", "ggpubr")
lapply(rmd_packages, require, character.only = TRUE)
source("01_main_analysis.R")
load(here("figures/exp_plots.RData"))
load(here("data/exp_results.RData"))

knitr::opts_chunk$set(echo=F, warning = F, message = F, results = "asis")
```

# Overview {.tabset .tabset-pills}

## Participants

A total of `r nrow(export)` participants were recruited through a survey posted on Prolific. `r sum(is.na(export$DQInclude))` were excluded as they did not complete the survey, and  `r table(export$VEG)[1]` were excluded as they are vegan/vegetarian,  and `r table(raw$DQInclude)[2]` were excluded for indicating that their results should not be included in the analysis. `r sum(raw$INTENT_QUALITY != 6 & raw$DQInclude == 1)` were excluded for failing to select the correct response in an attention check. The final sample (*N* = `r nrow(clean)`) ranged in age `r age_desc[1]` to `r age_desc[2]` (*M~age~* = `r papaja::printnum(age_desc[3])`, *SD* = `r papaja::printnum(age_desc[4])`). The participants were predominantly female (`r papaja::printnum(gender_freq[2])`%). The participants received £0.35 ($0.45) for successfully completing the task.

## Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all *p*’s > .05).

```{r rand-check}
as_tibble(random_check, .name_repair = "unique") %>%
  kable(escape = F, col.names = c("Item", "Dynamic", "Static", "No norm", "Significance test"),
            caption="Randomisation check") %>%
  kable_styling()
```

## Correlations

```{r}
kable(measure.tib, caption = "Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures") %>%
  kable_styling()
```


# Confirmatory analyses

## 1.	Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing their meat consumption (compared to static norm)? 

### Effect of condition on interest in reducing meat consumption

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from *M~diff~* = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

```{r, fig.show='asis', fig.cap="World cloud of participants response to text"}
comb_cloud <- rquery.wordcloud(comb_text, type ="text", lang = "english", min.freq = 5, excludeWords = c("reasons", "eating", "also", "meat", "people", "think", "much", "become", "due", "lot", "less", "eat", "consumption"))
```

```{r wordcloud}
cloud_freq <- head(comb_cloud$freqTable, 20)
apa_table(cloud_freq, caption = "Most frequent words in text", escape = F, row.names = F)
```

The mean interest for participants in the dynamic norm condition was *M* = `r papaja::printnum(outcomes_desc[[1,3]])` (*SD* = `r papaja::printnum(outcomes_desc[[1,4]])`), and the mean interest in the static norm condition was *M* = `r papaja::printnum(outcomes_desc[[2,3]])` (*SD* = `r papaja::printnum(outcomes_desc[[2,4]])`). The mean interest in the no norm condition was *M* = `r papaja::printnum(outcomes_desc[[3,3]])` (*SD* = `r papaja::printnum(outcomes_desc[[3,4]])`).

There was no difference in interest in reducing meat consumption between the dynamic norm (*M* = `r papaja::printnum(outcomes_desc[[1,3]])`, *SD* = `r papaja::printnum(outcomes_desc[[1,4]])`) and static norm (*M* = `r papaja::printnum(outcomes_desc[[2,3]])`, *SD* = `r papaja::printnum(outcomes_desc[[2,4]])`) conditions, `r ls_interest$full_result$DYST`, *d* = `r H1.effect$DYST$d`, $B_{\text{HN}(0, 0.69)}$ = `r papaja::printnum(H1.effect$DYST.Bf)`, RR[`r H1.effect$rr$RR$sd[1]`, `r H1.effect$rr$RR$sd[2]`].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (*M* = `r papaja::printnum(outcomes_desc[[3,3]])`, *SD* = `r papaja::printnum(outcomes_desc[[3,4]])`) and did not differ from those in the dynamic-norm condition `r ls_interest$full_result$DYNO`, *d* = `r H1.effect$DYNO$d`, or the static-norm condition `r ls_interest$full_result$STNO`, *d* = `r H1.effect$STNO$d`. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions `r ls_interest$full_result$DYCONT`. 

```{r, results='markup'}
apa_table(ls_interest$table, caption = "Meat consumption by condition contrasts", escape = F)
```


### Effect of demographic variables and condition on interest

Political left-wing participants were more interested than were right wing participants, `r pol.interest_out$statistic$POLITICS`, and women were more interested than were men, `r gen.interest_out$full_result`. When we controlled for these factors, the effect of the dynamic-norm condition (compared with that of the static-norm condition) on interest in eating less meat was `r demoreg.out$full_result$conditionbiStatic`. 

```{r, results='markup'}
apa_table(demoreg.out$table, caption = "Regression coefficients of demographic variables on interest", escape = F)
```

## 2. Will participants in the dynamic norm condition be more likely (than static norm and control) to predict a future decrease in meat consumption in the UK? {.tabset .tabset-pills} 

I modeled H2 using a half-normal distribution with a mean of 0 and SD of *M~diff~* = 0.40. The plausible maximum effect was set at twice the predicted effect of *M~diff~* = 0.80. A Bayes factor was calculated for each test. 

```{r}
kable(future, caption = "Expectations of future meat consumption", digits = 2, col.names = c("Condition", "$n$", rep(c("$M$", "$SD$"), 3))) %>%
  kable_styling() %>%
  add_header_above(c(" " = 2, "Future Norm" = 2, "Preconformity" = 2, "Combined" = 2))
```

#### **Measure of perception of change:** "In the next 5 years, I expect meat consumption in the UK to…"

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, `r ls_change$full_result$DYST`, *d* = `r H2change.effect$DYST$d`, $B_{\text{HN}(0, 0.40)}$ = `r papaja::printnum(H2change.effect$DYST.Bf)`, RR[`r H2change.effect$rr$RR$sd[1]`, `r H2change.effect$rr$RR$sd[2]`]

```{r}
kable(ls_change$table, caption = "Perception change contrasts", escape = F) %>%
  kable_styling()
```

#### **Measure of preconformity:** "In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?"

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (*M* = `r papaja::printnum(future[[1,5]])`, *SD* = `r papaja::printnum(future[[1,6]])`) and static norm (*M* = `r papaja::printnum(future[[2,5]])`, *SD* = `r papaja::printnum(future[[2,6]])`) conditions, `r ls_preconformity$full_result$DYST`, *d* = `r H2preconformity.effect$DYST$d`, $B_{\text{HN}(0, 0.40)}$ = `r papaja::printnum(H2preconformity.effect$DYST.Bf)`, RR[`r H2preconformity.effect$rr$RR$sd[1]`, `r H2preconformity.effect$rr$RR$sd[2]`].

```{r}
kable(ls_preconformity$table, caption = "Preconformity contrasts", escape = F) %>%
  kable_styling()
```

#### **Combined**

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (*M* = `r papaja::printnum(future[[1,7]])`, *SD* = `r papaja::printnum(future[[1,8]])`) and static norm (*M* = `r papaja::printnum(future[[2,7]])`, *SD* = `r papaja::printnum(future[[2,8]])`) conditions, `r ls_comb_future$full_result$DYST`, *d* = `r H2comb_future.effect$DYST$d`, $B_{\text{HN}(0, 0.40)}$ = `r papaja::printnum(H2comb_future.effect$DYST.Bf)`, RR[`r H2comb_future.effect$rr$RR$sd[1]`, `r H2comb_future.effect$rr$RR$sd[2]`].

```{r}
kable(ls_comb_future$table, caption = "Combined contrasts", escape = F) %>%
  kable_styling()
```

# Secondary analyses

## 1. Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions? 

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

```{r perceptions of static norm and INF}
TOSTtwo.sci(m1 = secondary[[1,4]], m2 = secondary[[2,4]], sd1 = secondary[[1,6]], sd2 = secondary[[2,6]], n1 = secondary[[1,2]], n2 = secondary[[2,2]], low_eqbound = -5, high_eqbound = 5, plot = F)
```

### 2. Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions? 

The SESOI for difference in number of meals is ± 2 meals.

```{r construal of meat cons and INF}
TOSTtwo.sci(m1 = secondary[[1,7]], m2 = secondary[[2,7]], sd1 = secondary[[1,8]], sd2 = secondary[[2,8]], n1 = secondary[[1,2]], n2 = secondary[[2,2]], low_eqbound = -5, high_eqbound = 5, plot = F)
```


# Exploratory Analyses

## 3. Does dynamic norm (versus static or no norm) information increase participants’ positive attitude, intentions, and expectations to reduce their meat consumption? {.tabset .tabset-pills}

### Convergence

```{r, fig.show='asis', fig.cap="Traceplots of regression parameters"}
h3.traceplots
```

### Estimates
    
```{r H3-SEM}
h3.table %>%
  kable(format = "html", caption = "Posterior results for simple model (H3)", escape = F) %>% 
  add_header_above(c(" " = 1, "Model 1: Uninformative priors$^a$" = 5, "Model 2: Weakly informative priors$^b$" = 6, "Model 3: Moderately informative priors$^c$" = 6)) %>%
  kable_styling() %>%
  footnote(general_title = "Note.",
           general = "PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size",
           alphabet = c(paste0("ppp = ", h3.global[[1]][[3]]), paste0("ppp = ", round(h3.global[[2]][[3]], 3)), paste0("ppp = ", h3.global[[3]][[3]]))) %>%
  landscape()
```

```{r, fig.show='asis', fig.cap="Posterior uncertainty intervals"}
h3_intervals.plot
```

## 4. How do demographic factors such as age, gender, and political position predict primary dependent variables relating to meat consumption? {.tabset .tabset-pills}

### Convergence

```{r, fig.show='asis', fig.cap="Traceplots for estimated regression parameters"}
h4.traceplots
```

### Estimates

```{r full}
h4.table %>% 
  kable(format = "html", caption = "Posterior results for full model (H4)", row.names = F, escape = F) %>%
  kable_styling() %>%
  add_header_above(c(" " = 1, "Model 1: Uninformative priors (ppp = 0.486)" = 5, "Model 2: Informative priors (ppp = 0.471)" = 6, "Model 3: Informative priors (ppp = 0.421)" = 6)) %>%
  pack_rows("Interest", 1, 4) %>%
  pack_rows("Attitudes", 5, 8) %>%
  pack_rows("Intentions", 9, 12) %>%
  pack_rows("Expectations", 13, 16)
```

## 5. How does age interact with norm condition to influence dependent variables? {.tabset .tabset-pills}

### Convergence

```{r, fig.show='asis', fig.cap="Traceplots of regression parameters"}
h5.traceplots
```

### Estimates

```{r interaction}
interact.table %>% 
  kable(format = "html", caption = "Posterior results for multi-sample analysis by age (H4)", row.names = F, escape = F) %>%
  add_header_above(c(" " = 1, "Model 1: Uninformative priors" = 5, "Model 2: Informative priors" = 6, "Model 3: Informative priors" = 6)) %>%  
  kable_styling() %>%
  pack_rows("Older", 1, 4) %>%
  pack_rows("Younger", 5, 8)
```

# Environment and data {.tabset .tabset-pills}

### Session information
```{r, results='markup'}
devtools::session_info()
```

### Codebook
```{r codebook}
codebook::codebook_table(clean) %>% kable()
```

